Impact of bedaquiline resistance probability on treatment decision for rifampicin-resistant TB

SUMMARY BACKGROUND Accurate diagnosis of bedaquiline (BDQ) resistance remains challenging. A Bayesian approach expresses this uncertainty as a probability of BDQ resistance (prBDQR) with a 95% credible interval. We investigated how prBDQR information influences BDQ prescribing decisions. METHOD We performed a discrete choice experiment with 55 international rifampicin-resistant tuberculosis physicians. We employed mixed-effects multinomial logistic regression to quantify the effect of prBDQR, patient attributes, and contextual factors on the decision to continue BDQ or not when sequencing results become available. RESULTS PrBDQR was the most influential factor for BDQ decision-making, three times greater than treatment response. Each percentage point increase in prBDQR resulted in 8.2% lower odds (OR 0.92, 95% CI 0.90–0.93) of continuing BDQ as a fully effective drug and 5.0% lower odds (OR 0.95, 95% CI 0.94–0.96) of continuing it but not counting it as an effective drug. The most favourable patient profile for prescribing BDQ as a fully effective drug was a patient receiving the BPaLM regimen (BDQ, pretomanid, linezolid and moxifloxacin) with low prBDQR, good 1-month treatment response, fluoroquinolone-susceptible TB, and no prior BDQ treatment. Physicians with higher discomfort with uncertainty and more years of experience with BDQ were more inclined to stop BDQ. CONCLUSION Given the uncertainty of genotype-phenotype associations, physicians valued prBDQR for BDQ decision-making in rifampicin-resistant TB treatment.

of continuing BDQ as a fully effective drug and 5.0% lower odds (OR 0.95, 95% CI 0.94-0.96) of continuing it but not counting it as an effective drug.The most favourable patient profile for prescribing BDQ as a fully effective drug was a patient receiving the BPaLM regimen (BDQ, pretomanid, linezolid and moxifloxacin) with low prBDQ R , good 1-month treatment response, fluoroquinolone-susceptible TB, and no prior BDQ treatment.Physicians with higher discomfort with uncertainty and more years of experience with BDQ were more inclined to stop BDQ.

C O N C L U S I O N :
Given the uncertainty of genotypephenotype associations, physicians valued prBDQ R for BDQ decision-making in rifampicin-resistant TB treatment.K E Y W O R D S : discrete choice experiment; clinical decisionmaking; treatment uncertainty Bedaquiline (BDQ) has become a key drug for treating rifampicin-resistant TB (RR-TB).Unfortunately, BDQ drug susceptibility testing (DST) does not keep pace with its widespread use.The development of a rapid genotypic DST is hindered by an incomplete understanding of the genomic basis of BDQ resistance, with many Rv0678 variants being of 'uncertain significance.' 1 This uncertainty poses challenges for physicians in interpreting test results.
A Bayesian approach to predict the probability of BDQ resistance (prBDQ R ) from genotypic data was recently proposed as an alternative solution. 2The prBDQ R estimates the likelihood of BDQ resistance for a specific Rv0678 variant, with a 95% credible interval indicating the range of the true prBDQ R value.Accompanying the report of an Rv0678 variant with its prBDQ R could assist physicians in their decisionmaking.This probabilistic approach is, however, new to physicians who are used to binary DST results.To explore the clinical application of prBDQ R , we quantified the influence of prBDQ R information on physicians' judgement of the clinical value of BDQ for specific patients by investigating decisions to prescribe BDQ.

Study design
A discrete choice experiment (DCE) was conducted to quantify the relative importance of prBDQ R and other factors in physicians' decisions to continue BDQ in the treatment regimen after receiving next-generation sequencing (NGS) results.
[5] Step 1: Selection of DCE attributes and levels We started from a comprehensive list of attributes generated by a qualitative study that explored factors influencing physicians' decision-making regarding the use of BDQ. 6 The final selection of attributes was discussed by an expert panel using the following criteria for DCE factors: 1) influencing the decision to prescribe BDQ, 2) being 'tradeable' by not having an extreme impact on decision-making and 3) being independent (not correlated with) other attributes. 4,7he attribute levels were chosen to ensure clinical plausibility and 'tradeability' (i.e., not dominating decision-making). 7,8The expert panel agreed on a final set of six patient attributes and their corresponding levels (Table 1), five physician characteristics and three setting characteristics.
Step 2: Experimental design and survey piloting The DCE was designed as a single-profile, threecategory-response choice experiment in which physicians were presented with hypothetical patients using narrative vignettes (Supplementary Data 1).We employed the statistical software package JMP Pro 17 (SAS Institute, Cary, NC, USA) to generate a fractional factorial experimental design with 42 unique patient profiles, 9 partitioned into three blocks of 14 profiles administered to different participants to reduce the burden on them.All patients described in the vignettes had initiated a BDQ-containing ambulatory regimen for RR-TB before the NGS results were available.Clinical information not part of the six patient attributes was included as fixed information in all vignettes.At the 1-month follow-up visit, the approximate time when NGS results become available, physicians were asked for each hypothetical patient whether they would 1) continue BDQ as a fully effective drug, 2) continue BDQ but not count it as a fully effective drug, or 3) stop BDQ, and to indicate the certainty of their answers on a scale of 10.To assess reliability, one survey vignette was presented twice.The survey was pilot-tested with four physicians.
Step 3: Data collection Physicians with experience in managing RR-TB patients and directly involved in prescribing RR-TB treatment were eligible to participate in the DCE study.Recruitment occurred through invitations to participants and alumni of the course "Clinical Decision-making for Drug-resistant Tuberculosis", organised by the Institute of Tropical Medicine (Antwerp, Belgium) 10 , invitations to RR-TB physicians identified by the study investigators, and snowball sampling.The DCE was performed online using the Qualtrics Survey platform.Following informed consent, participants were asked about their characteristics and randomly assigned to one of the three blocks of vignettes.

Sample size
After recruiting the first 25 participants, we performed an interim analysis to simulate the number of participants required to have 80% power to detect the effect of prBDQ R at the overall significance level of 5%. 11,12Based on these results, a final sample size of at least 42 participants was required to detect the effect of

Data analysis
To determine the relative importance of each attribute and level in the BDQ prescribing decision-making process, we modelled the three-category outcome (stop BDQ, continue BDQ but not count it as a fully effective drug, or continue BDQ as a fully effective drug) as a function of the attribute levels using a mixed-effects multinomial logistic model.A three-level outcome was chosen based on the results of a qualitative study. 6We started with the full model for which the design was constructed, involving all attribute main effects and the interaction terms between prBDQ R and resistance profile, ambulatory regimen, BDQ exposure history and credible interval of prBDQ R .Important interaction terms were chosen by backward selection from the full model using Akaike's information criterion (AIC).Likelihood ratio tests were used to quantify each attribute's overall significance.We expressed each attribute's relative importance by the logworth statistic, i.e., log 10 (P-value of the likelihood ratio test). 13We explored the influence of physician and setting characteristics by adding those variables and their interaction terms with prBDQ R to the regression model.The interaction terms remained in the model if the model's AIC was lower than the model with only main effects.
Intra-class correlation was calculated to estimate the proportion of the variance explained by the preference heterogeneity among participants. 14The content validity of the DCE was assessed by comparing the sign of the estimated model parameter with the a priori hypothesis for each attribute. 15Measurement reliability was assessed by test-retest stability for the vignette that was presented twice. 15A sensitivity analysis was conducted to assess whether excluding choices with certainty lower than 4 points out of 10 affected the model's findings.The free-text comments of participants on each patient vignette and choice task were analysed qualitatively using a thematic approach to understand the reasoning behind physicians' treatment decisions.

Ethics approval and consent to participate
The study protocol was approved by the Research Ethics Committee of the University Hospital of the University of Antwerp, Antwerp, Belgium (Reference number 3395).All participants provided electronic informed consent before study participation.

Participant characteristics
Fifty-five participants were enrolled between 25 May 2022 and 16 May 2023.Participant characteristics are described in Table 2. Of the 48 participants who completed the test-retest exercise, 28 (58%) responded similarly to the repeated choice task.The DCE tasks were rated as quite difficult by 38% of the participants and very difficult by 14%.

Effect of prBDQ R and patient characteristics on physicians' decision-making
The final model was a mixed-effects multinomial logistic model with a continuous prBDQ R variable and included only main effects.The effects of prBDQ R , response to treatment at 1-month follow-up, a pre-extensively drug-resistant TB (pre-XDR-TB) resistance profile, and BDQ exposure history were statistically significant (Table 3).Notably, each percentage point increase in the prBDQ R was associated with 5.0% lower odds (odds ratio [OR] 0.95, 95% confidence interval [CI] 0.93-0.96) of prescribing BDQ but not counting it as an effective drug and 8.2% lower odds (OR 0.92, 95% CI 0.90-0.93) of prescribing BDQ as a fully effective drug, compared to not prescribing BDQ.The odds of continuing BDQ (either as a fully effective drug or not) also significantly decreased if a patient had no improvement in treatment response at 1-month followup, had pre-XDR-TB, or had a prior history of exposure to BDQ.A narrow credible interval of prBDQ R and the type of initial RR-TB regimen had no significant effect on physicians' decision-making.The intra-class correlation was 0.37 for 'prescribe BDQ as a fully effective drug' vs 'not prescribe BDQ' and 0.43 for 'prescribe BDQ but not as a fully effective drug' vs 'not prescribe BDQ,' implying that physician heterogeneity could explain a moderate amount of variation in treatment decisions.
The effect estimates show that the most favourable patient profile for the decision to continue BDQ as a fully effective drug is a patient with good response to treatment at 1-month follow-up, Mycobacterium tuberculosis (Mtb) susceptible to fluoroquinolones (FQs), no history of prior exposure to BDQ, use of BPaLM (bedaquiline, pretomanid, linezolid and moxifloxacin) as the initial regimen, and a narrow credible interval of prBDQ R .Physicians only stopped BDQ for such patients when the prBDQ R reached 75% (Figure 1A).In contrast, for patients with pre-XDR-TB who failed to improve clinically after one month of treatment and who were treated with BDQ during a previous RR-TB episode, physicians preferred to stop BDQ when the prBDQ R reached 40% (Figure 1B).For a patient with a profile in between these two extremes, physicians were likely to continue BDQ as a fully effective drug if the PrBDQ R was below 35% and stop BDQ when the prBDQ R exceeded 65%.If the PrBDQ R was between 35% and 65%, they preferred to continue BDQ but not as an effective drug (Figure 1C). Figure 2 illustrates the importance of the different attributes on the logworth scale relative to the prBDQ R attribute.The prBDQ R was approximately three times more important than the treatment response and ten times more than the patient's resistance profile.The attribute credible interval of prBDQ R played the least important role in physicians' decisions to continue or stop BDQ.

Influence of physician and setting characteristics on treatment choice
Physician-and setting-related variables were added to the model to explore observed heterogeneity among physicians in their BDQ prescription decision, particularly differences in the perceived relative importance of prBDQ R (Supplementary Data 3).Physicians with more years of experience in using BDQ (OR 0.62-0.71for every year increase in BDQ experience) and those with higher discomfort with uncertainty (OR 0.86-0.89for every 1-point increase in uncertainty score) were less likely to continue BDQ.Interactions between prBDQ R and physician-and setting-related characteristics were not identified.

Sensitivity analysis
There were 42 out of 653 choices made for which participants rated their certainty lower than or equal to 4 out of 10. Results were similar to those of the current model when these choices were excluded (Supplementary Data 4).

Qualitative comments
Physicians remarked that they opted to continue BDQ but not as an effective drug when there was a discordance between patient response and prBDQ R , when prBDQ R was low for a patient who was previously treated with a BDQ-containing regimen, or when the patient's Mtb strain was FQ-resistant.Some physicians found it challenging to use prBDQ R in their treatment decisions because they were not familiar with employing NGS results in patient management or had limited experience with managing BDQ-resistant TB.Several physicians expressed uncertainty about the inference of BDQ phenotype from genotypic results.The full results of the qualitative analysis and accompanying quotes are provided in Supplementary Data 5.

DISCUSSION
We performed a DCE to quantify the effect of the prBDQ R on physicians' decision-making for RR-TB management.The prBDQ R was the most significant factor influencing physicians' decisions to prescribe BDQ.The most favourable patient profile for prescribing BDQ as a fully effective drug when sequencing results become available is a patient with a low prBDQ R , a good response after 1 month of treatment, FQ-susceptible TB, no prior BDQ use, and receiving BPaLM.Studies of medical decision-making showed that sources of uncertainty include the indeterminacy of the treatment outcome, and ambiguity and complexity of risk information. 16In the context of BDQ decisionmaking once genomic DST results are available, uncertainty arises from the many factors affecting RR-TB treatment response, challenges in inferring BDQ phenotype from genotype, and the limited understanding of the effect of the presence of a variant in the Rv0678 gene on RR-TB treatment outcomes.Until the impact of the presence of a specific genomic variant in Mtb on the effectiveness of BDQ for a particular patient can be determined with sufficient certainty, communicating prBDQ R values could be a helpful approach to predict the clinical usefulness of BDQ for individual patients and promote judicious BDQ use.
8][19] We observed that physicians who reported high discomfort with uncertainty were more likely to modify or intensify the treatment regimen than those with lower discomfort.This is consistent with previous findings that physicians may favour action over inaction in instances of high uncertainty 20 and that physicians' low tolerance for uncertainty may lead to ordering more tests and offering interventions. 21ur study confirmed two key findings of a previous qualitative study.Firstly, when making treatment decisions, prBDQ R should not be viewed as stand-alone information but should be interpreted in the context of patient characteristics.Decision thresholds for prBDQ R are not fixed, but vary with changing patient characteristics. 6For example, the DCE model predicts that at a 50% prBDQ R , physicians would continue BDQ as a fully effective drug if the patient had a favourable profile, but would stop BDQ if the patient had pre-XDR-TB and did not improve clinically after 1 month of treatment and had previously been treated with BDQ.Secondly, continuing BDQ but not counting it as a fully effective drug by strengthening the regimen is a valid option when the patient has risk factors for both good and poor treatment outcomes.This approach maximises the likelihood that a patient will benefit from receiving BDQ in the event that BDQ is still effective and minimises the risk of amplifying resistance if the Mtb strain were to be resistant to BDQ, which would result in fewer than three effective drugs in the BPaLM treatment regimen. 22The option to strengthen the regimen further highlights the importance of using NGS to obtain a comprehensive resistance profile, which is needed to design an evidence-based, robust treatment regimen. 23his study must also be interpreted considering its limitations.First, we could only investigate a limited number of attributes and individual traits, while in reality, the range of factors physicians consider when making decisions may be much more complex.Second, the test-retest reliability of the DCE was only moderate, indicating that the DCE tasks may be too complicated for some participants and that there may be a learning curve effect. 24,25Third, the convenience sample may have induced selection bias.Fourth, the prBDQ R of a specific variant in Rv0678 must be interpreted with information on mmpL5, as epistatic interactions can occur. 26Fifth, numerous challenges must be overcome before prBDQ R can be implemented in clinical care.Frequent updates are required to accurately estimate prBDQ R for individual genomic variants as new evidence emerges.Finally, genome sequencing is needed, which requires considerable efforts to build laboratory and bioinformatics capacity in high RR-TB burden countries. 27,28

CONCLUSIONS
The novel concept of prBDQ R helps communicate the consequence of a specific Rv0678 gene variant to physicians.The decision thresholds for prBDQ R are not fixed, but depend on patient characteristics.Physicians often opted to continue BDQ and strengthen the regimen when the prBDQ R , in combination with patient characteristics, suggested uncertainty about its clinical value.Future studies should investigate whether applying the prBDQ R in practice promotes the optimal use of BDQ and improves treatment outcomes.

Figure 1 .
Figure 1.Predicted probability of treatment decision by prBDQ R for A) a patient with MDR-TB, good treatment response, no history of exposure to BDQ, initiated on BPaLM regimen, and narrow credible intervals for prBDQ R ; B) a patient with pre-XDR-TB, no improvement in treatment response, previously exposed to BDQ, initiated on the 9-month regimen, and narrow credible intervals for prBDQ R ; C) a patient with MDR-TB þ PZA þ EMB resistance, good treatment response, previously exposed to BDQ, initiated on the 9-month regimen, and narrow credible intervals for prBDQ R .BDQ ¼ bedaquiline; prBDQ R ¼ probability of BDQ resistance; BPaLM ¼ BDQ, pretomanid, linezolid, moxifloxacin; MDR-TB ¼ multidrug-resistant TB; PZA ¼ pyrazinamide; EMB ¼ ethambutol.

Figure 2 .
Figure 2. Relative importance of the six attributes compared to the most important attribute 'probability of BDQ resistance' (normalised logworth of 100%).BDQ ¼ bedaquiline.

Table 1 .
DCE attributes and their levels.

Table 2 .
Characteristics of physicians participating in the DCE (n ¼ 55).
Perception of BDQ: "I believe that the evidence is robust to prescribe a BDQ-containing regimen to all RR-TB patients who have no contraindication for BDQ" †

Table 3 .
Effect of patient characteristics on physicians' decision-making about prescribing BDQ for RR-TB patients.